Literature DB >> 3998808

Simple- and complex-cell response dependences on stimulation parameters.

H Spitzer, S Hochstein.   

Abstract

We studied the response time course and amplitude dependence on stimulation parameters in cat cortical visual neurons to determine their receptive-field spatial-summation characteristics. Response poststimulus time (PST) histograms of cortical simple cells to contrast-reversal grating stimulation generally have a single peak for each stimulus temporal cycle, though the responses appear rectified. In response to contrast-reversal grating stimulation the general PST histogram time course for complex cells is two peaks, though often these peaks are of different amplitudes. The time course of complex-cell responses, and the ratio of these two response peaks often varies with stimulation parameters. The appearance of a single response peak in simple cells is reflected in the dominance of the odd harmonic Fourier portion, whereas the half-wave rectification leads to a considerable even harmonic portion. Still, this even portion is never significantly greater than the odd portion. When complex cell PST histograms have two nearly equal peaks, Fourier transformation reveals almost only even harmonic components. When the histogram contains two peaks of unequal amplitude Fourier analysis reveals large odd and even components. An even:odd Fourier harmonic portion ratio larger than 1 may be seen as a defining characteristic of complex cells, differentiating them from simple cells. Histograms with two unequal peaks appear "mixed," containing something of the "pure" single-peaked response and something of the pure double-peaked response. The degree to which the response is mixed may be measured by the ratio of the even:odd portion amplitudes. There is a great degree of variability with stimulation parameters (both spatial phase and spatial frequency) of the time course of mixed responses as opposed to the case of responses that have two equal peaks independent of stimulation grating phase and frequency. In both simple and complex cells there is a close coincidence of the spatial frequency ranges over which the even and odd portions are substantial, though many complex cells show a periodic variation of the even:odd portions ratio. This spatial-frequency dependence differs from that of LGN Y-cells where the odd portion dominates at low spatial frequencies and the even portion at high spatial frequencies. The ratio of even-to-odd portion cut-off is close to 3:1 in all Y-cells, a characteristic we did not find in cortical simple or complex cells. We suggest, therefore, that the nonlinearity of these complex cells does not derive from that of Y-cells.(ABSTRACT TRUNCATED AT 400 WORDS)

Mesh:

Year:  1985        PMID: 3998808     DOI: 10.1152/jn.1985.53.5.1244

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  19 in total

1.  How simple cells are made in a nonlinear network model of the visual cortex.

Authors:  D J Wielaard; M Shelley; D McLaughlin; R Shapley
Journal:  J Neurosci       Date:  2001-07-15       Impact factor: 6.167

2.  An egalitarian network model for the emergence of simple and complex cells in visual cortex.

Authors:  Louis Tao; Michael Shelley; David McLaughlin; Robert Shapley
Journal:  Proc Natl Acad Sci U S A       Date:  2003-12-26       Impact factor: 11.205

3.  A nonlinear model of the behavior of simple cells in visual cortex.

Authors:  Miguel A García-Pérez
Journal:  J Comput Neurosci       Date:  2004 Nov-Dec       Impact factor: 1.621

4.  'Simplification' of responses of complex cells in cat striate cortex: suppressive surrounds and 'feedback' inactivation.

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Journal:  J Physiol       Date:  2006-05-18       Impact factor: 5.182

5.  Dimensionally-reduced visual cortical network model predicts network response and connects system- and cellular-level descriptions.

Authors:  Louis Tao; Andrew T Sornborger
Journal:  J Comput Neurosci       Date:  2009-10-06       Impact factor: 1.621

6.  Spatial and temporal features of synaptic to discharge receptive field transformation in cat area 17.

Authors:  Lionel G Nowak; Maria V Sanchez-Vives; David A McCormick
Journal:  J Neurophysiol       Date:  2009-11-11       Impact factor: 2.714

7.  Improved dimensionally-reduced visual cortical network using stochastic noise modeling.

Authors:  Louis Tao; Jeremy Praissman; Andrew T Sornborger
Journal:  J Comput Neurosci       Date:  2011-08-27       Impact factor: 1.621

8.  Response histogram shapes and tuning curves: the predicted responses of several cortical cell types to drifting gratings stimuli.

Authors:  D Malonek; H Spitzer
Journal:  Biol Cybern       Date:  1989       Impact factor: 2.086

9.  Contrast and spatial variables in texture segregation: testing a simple spatial-frequency channels model.

Authors:  A Sutter; J Beck; N Graham
Journal:  Percept Psychophys       Date:  1989-10

10.  A synaptic signature for ON- and OFF-center parasol ganglion cells of the primate retina.

Authors:  Joanna D Crook; Orin S Packer; Dennis M Dacey
Journal:  Vis Neurosci       Date:  2014-01       Impact factor: 3.241

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